{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import scipy.io\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import keras\n",
    "from keras.preprocessing import image\n",
    "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Activation\n",
    "from keras.layers import Conv2D, AveragePooling2D\n",
    "from keras.models import Model, Sequential\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from keras import metrics\n",
    "\n",
    "from keras.models import model_from_json\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/\n",
    "mat = scipy.io.loadmat('wiki_crop/wiki.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"dob\", \"photo_taken\", \"full_path\", \"gender\", \"name\", \"face_location\", \"face_score\", \"second_face_score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "instances = mat['wiki'][0][0][0].shape[1]\n",
    "\n",
    "df = pd.DataFrame(index = range(0,instances), columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in mat:\n",
    "    if i == \"wiki\":\n",
    "        current_array = mat[i][0][0]\n",
    "        for j in range(len(current_array)):\n",
    "            #print(columns[j],\": \",current_array[j])\n",
    "            df[columns[j]] = pd.DataFrame(current_array[j][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dob</th>\n",
       "      <th>photo_taken</th>\n",
       "      <th>full_path</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>face_location</th>\n",
       "      <th>face_score</th>\n",
       "      <th>second_face_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>723671</td>\n",
       "      <td>2009</td>\n",
       "      <td>[17/10000217_1981-05-05_2009.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Sami Jauhojärvi]</td>\n",
       "      <td>[[111.29109473290997, 111.29109473290997, 252....</td>\n",
       "      <td>4.300962</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>703186</td>\n",
       "      <td>1964</td>\n",
       "      <td>[48/10000548_1925-04-04_1964.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Dettmar Cramer]</td>\n",
       "      <td>[[252.48330229530742, 126.68165114765371, 354....</td>\n",
       "      <td>2.645639</td>\n",
       "      <td>1.949248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711677</td>\n",
       "      <td>2008</td>\n",
       "      <td>[12/100012_1948-07-03_2008.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Marc Okrand]</td>\n",
       "      <td>[[113.52, 169.83999999999997, 366.08, 422.4]]</td>\n",
       "      <td>4.329329</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>705061</td>\n",
       "      <td>1961</td>\n",
       "      <td>[65/10001965_1930-05-23_1961.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[Aleksandar Matanović]</td>\n",
       "      <td>[[1, 1, 634, 440]]</td>\n",
       "      <td>-inf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>720044</td>\n",
       "      <td>2012</td>\n",
       "      <td>[16/10002116_1971-05-31_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[Diana Damrau]</td>\n",
       "      <td>[[171.61031405173117, 75.57451239763239, 266.7...</td>\n",
       "      <td>3.408442</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dob  photo_taken                          full_path  gender  \\\n",
       "0  723671         2009  [17/10000217_1981-05-05_2009.jpg]     1.0   \n",
       "1  703186         1964  [48/10000548_1925-04-04_1964.jpg]     1.0   \n",
       "2  711677         2008    [12/100012_1948-07-03_2008.jpg]     1.0   \n",
       "3  705061         1961  [65/10001965_1930-05-23_1961.jpg]     1.0   \n",
       "4  720044         2012  [16/10002116_1971-05-31_2012.jpg]     0.0   \n",
       "\n",
       "                     name                                      face_location  \\\n",
       "0       [Sami Jauhojärvi]  [[111.29109473290997, 111.29109473290997, 252....   \n",
       "1        [Dettmar Cramer]  [[252.48330229530742, 126.68165114765371, 354....   \n",
       "2           [Marc Okrand]      [[113.52, 169.83999999999997, 366.08, 422.4]]   \n",
       "3  [Aleksandar Matanović]                                 [[1, 1, 634, 440]]   \n",
       "4          [Diana Damrau]  [[171.61031405173117, 75.57451239763239, 266.7...   \n",
       "\n",
       "   face_score  second_face_score  \n",
       "0    4.300962                NaN  \n",
       "1    2.645639           1.949248  \n",
       "2    4.329329                NaN  \n",
       "3        -inf                NaN  \n",
       "4    3.408442                NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove pictures does not include face\n",
    "df = df[df['face_score'] != -np.inf]\n",
    "\n",
    "#some pictures include more than one face, remove them\n",
    "df = df[df['second_face_score'].isna()]\n",
    "\n",
    "#check threshold\n",
    "df = df[df['face_score'] >= 3]\n",
    "\n",
    "#some records do not have a gender information\n",
    "df = df[~df['gender'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = ['dob','photo_taken','name','face_score','second_face_score','face_location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_path</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[17/10000217_1981-05-05_2009.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[12/100012_1948-07-03_2008.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[16/10002116_1971-05-31_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[02/10002702_1960-11-09_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[41/10003541_1937-09-27_1971.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           full_path  gender\n",
       "0  [17/10000217_1981-05-05_2009.jpg]     1.0\n",
       "2    [12/100012_1948-07-03_2008.jpg]     1.0\n",
       "4  [16/10002116_1971-05-31_2012.jpg]     0.0\n",
       "5  [02/10002702_1960-11-09_2012.jpg]     0.0\n",
       "6  [41/10003541_1937-09-27_1971.jpg]     1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram = df['gender'].hist(bins=df['gender'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     6580\n",
       "1.0    15575\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts().sort_index()\n",
    "#0: woman, 1: man"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of output classes:  2\n"
     ]
    }
   ],
   "source": [
    "classes = 2 #man woman\n",
    "print(\"number of output classes: \",classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_size = (224, 224)\n",
    "\n",
    "def getImagePixels(image_path):\n",
    "    img = image.load_img(\"wiki_crop/%s\" % image_path[0], grayscale=False, target_size=target_size)\n",
    "    x = image.img_to_array(img).reshape(1, -1)[0]\n",
    "    #x = preprocess_input(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pixels'] = df['full_path'].apply(getImagePixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_path</th>\n",
       "      <th>gender</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[17/10000217_1981-05-05_2009.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[12/100012_1948-07-03_2008.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[92.0, 97.0, 91.0, 89.0, 94.0, 90.0, 91.0, 96....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[16/10002116_1971-05-31_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[61.0, 30.0, 10.0, 61.0, 30.0, 10.0, 61.0, 30....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[02/10002702_1960-11-09_2012.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[97.0, 122.0, 178.0, 97.0, 122.0, 178.0, 97.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[41/10003541_1937-09-27_1971.jpg]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[190.0, 189.0, 194.0, 204.0, 203.0, 208.0, 203...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           full_path  gender  \\\n",
       "0  [17/10000217_1981-05-05_2009.jpg]     1.0   \n",
       "2    [12/100012_1948-07-03_2008.jpg]     1.0   \n",
       "4  [16/10002116_1971-05-31_2012.jpg]     0.0   \n",
       "5  [02/10002702_1960-11-09_2012.jpg]     0.0   \n",
       "6  [41/10003541_1937-09-27_1971.jpg]     1.0   \n",
       "\n",
       "                                              pixels  \n",
       "0  [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...  \n",
       "2  [92.0, 97.0, 91.0, 89.0, 94.0, 90.0, 91.0, 96....  \n",
       "4  [61.0, 30.0, 10.0, 61.0, 30.0, 10.0, 61.0, 30....  \n",
       "5  [97.0, 122.0, 178.0, 97.0, 122.0, 178.0, 97.0,...  \n",
       "6  [190.0, 189.0, 194.0, 204.0, 203.0, 208.0, 203...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df['gender'].values\n",
    "target_classes = keras.utils.to_categorical(target, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features = df['pixels'].values\n",
    "features = []\n",
    "\n",
    "for i in range(0, df.shape[0]):\n",
    "    features.append(df['pixels'].values[i])\n",
    "\n",
    "features = np.array(features)\n",
    "features = features.reshape(features.shape[0], 224, 224, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22155, 224, 224, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "features /= 255 #normalize in [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(features, target_classes, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VGG-Face model\n",
    "model = Sequential()\n",
    "model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))\n",
    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(ZeroPadding2D((1,1)))\n",
    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "model.add(Convolution2D(4096, (7, 7), activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Convolution2D(4096, (1, 1), activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Convolution2D(2622, (1, 1)))\n",
    "model.add(Flatten())\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pre-trained weights of vgg-face model. \n",
    "#you can find it here: https://drive.google.com/file/d/1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo/view?usp=sharing\n",
    "#related blog post: https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/\n",
    "model.load_weights('vgg_face_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#freeze all layers of VGG-Face except last 7 one\n",
    "for layer in model.layers[:-7]:\n",
    "    layer.trainable = False\n",
    "\n",
    "base_model_output = Sequential()\n",
    "base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)\n",
    "base_model_output = Flatten()(base_model_output)\n",
    "base_model_output = Activation('softmax')(base_model_output)\n",
    "\n",
    "gender_model = Model(inputs=model.input, outputs=base_model_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check trainable layers\n",
    "if False:\n",
    "    for layer in model.layers:\n",
    "        print(layer, layer.trainable)\n",
    "    \n",
    "    print(\"------------------------\")\n",
    "    for layer in age_model.layers:\n",
    "        print(layer, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = keras.optimizers.SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "\n",
    "gender_model.compile(loss='categorical_crossentropy'\n",
    "                  , optimizer=keras.optimizers.Adam()\n",
    "                  #, optimizer = sgd\n",
    "                  , metrics=['accuracy']\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointer = ModelCheckpoint(\n",
    "    filepath='classification_gender_model.hdf5'\n",
    "    , monitor = \"val_loss\"\n",
    "    , verbose=1\n",
    "    , save_best_only=True\n",
    "    , mode = 'auto'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "enableFit = False\n",
    "\n",
    "if enableFit:\n",
    "    epochs = 250\n",
    "    batch_size = 256\n",
    "\n",
    "    for i in range(epochs):\n",
    "        print(\"epoch \",i)\n",
    "        \n",
    "        ix_train = np.random.choice(train_x.shape[0], size=batch_size)\n",
    "        \n",
    "        score = gender_model.fit(\n",
    "            train_x[ix_train], train_y[ix_train]\n",
    "            , epochs=1\n",
    "            , validation_data=(test_x, test_y)\n",
    "            , callbacks=[checkpointer]\n",
    "        )\n",
    "        \n",
    "        scores.append(score)\n",
    "        \n",
    "        from keras.models import load_model\n",
    "        gender_model = load_model(\"classification_gender_model.hdf5\")\n",
    "        \n",
    "        gender_model.save_weights('gender_model_weights.h5')\n",
    "        \n",
    "else:\n",
    "    #pre-trained weights for gender prediction: https://drive.google.com/file/d/1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk/view?usp=sharing\n",
    "    gender_model.load_weights(\"gender_model_weights.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_loss_change = []; loss_change = []\n",
    "for i in range(0, len(scores)):\n",
    "    val_loss_change.append(scores[i].history['val_loss'])\n",
    "    loss_change.append(scores[i].history['loss'])\n",
    "\n",
    "plt.plot(val_loss_change, label='val_loss')\n",
    "plt.plot(loss_change, label='train_loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing model on the testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6647/6647 [==============================] - 17s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07324957040103375, 0.9744245524655362]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loss and accuracy on validation set\n",
    "gender_model.evaluate(test_x, test_y, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = gender_model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1873,   98],\n",
       "       [  72, 4604]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "pred_list = []; actual_list = []\n",
    "\n",
    "for i in predictions:\n",
    "    pred_list.append(np.argmax(i))\n",
    "\n",
    "for i in test_y: \n",
    "    actual_list.append(np.argmax(i))\n",
    "\n",
    "confusion_matrix(actual_list, pred_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing model\n",
    "\n",
    "Feed an image to find the gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadImage(filepath):\n",
    "    test_img = image.load_img(filepath, target_size=(224, 224))\n",
    "    test_img = image.img_to_array(test_img)\n",
    "    test_img = np.expand_dims(test_img, axis = 0)\n",
    "    test_img /= 255\n",
    "    return test_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "picture = \"katy-3.jpg\"\n",
    "\n",
    "prediction = gender_model.predict(loadImage(picture))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender:  Woman\n"
     ]
    }
   ],
   "source": [
    "img = image.load_img(picture)#, target_size=(224, 224))\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "gender = \"Man\" if np.argmax(prediction) == 1 else \"Woman\"\n",
    "\n",
    "print(\"gender: \", gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
